Historic masonry mosques represent a highly vulnerable class of cultural heritage structures whose seismic fragility stems from their complex geometries, heterogeneous material composition, and rigid load-transfer mechanisms. This study presents a resilience-oriented seismic performance improvement of the historic masonry minaret of the Bayburt Grand Mosque, a structure with limited lateral deformation capacity that challenges the applicability of conventional strengthening measures. To address this limitation, a hybrid retrofitting strategy is introduced, integrating lead rubber bearings (LRBs) with supplemental viscous dampers (VDs) at the foundation level. This combined system—implemented for the first time in a historic masonry minaret—aims to enhance energy dissipation and displacement control through a minimally invasive and architecturally compatible approach. Finite element analyses (FEA), coupled with MATLAB-supported optimization routines, were used to calibrate isolator stiffness and damper coefficients. Three configurations were evaluated: fixed-base, LRB-isolated, and hybrid LRB–VD systems. Nonlinear time-history analyses (NTHAs) using the 1992 Erzincan (Otlukbeli) earthquake record quantified displacements, stress–strain responses, and damage progression. Results show that while base isolation mitigates seismic demand, it remains insufficient under restricted deformation capacity. The proposed hybrid system reduces peak horizontal displacements from 22 cm to 12 cm, limits drift ratios below 0.17 %, and lowers maximum tensile stresses from 4.890 MPa to 0.490 MPa—well below the masonry tensile strength of 0.880 MPa. Two iterative analytical design methodologies are additionally introduced to derive effective isolator stiffness and viscous damping coefficients, enabling systematic integration into resilience-focused evaluation frameworks. Overall, the study advances reliability-based, conservation-compatible retrofitting practices for historic masonry minarets and supports sustainable strategies for seismic risk mitigation.

Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.

Coal-dependent power systems must reduce cost volatility and emissions while maintaining reliable supply under rising demand. This study assesses whether a practical transition architecture, high-penetration photovoltaic (PV) generation combined with a dispatchable coal unit and grid support, can improve techno-economic and environmental performance without sacrificing feasibility. A grid-connected PV-coal-grid hybrid system was modelled and optimized in HOMER Pro, and a sensitivity campaign was conducted by varying coal fuel price, global horizontal irradiance (GHI), and load demand to test robustness and dispatch shifts. The least-cost feasible solution within the explored design space comprises 145 MW PV and a 75 MW coal power plant with grid interaction. Under baseline conditions, the optimized system achieves a net present cost (NPC) of $632 million and a levelized cost of electricity (COE) of $0.049/kWh. Sensitivity results show that increased GHI consistently reduces NPC and COE, while coal price increases drive greater PV utilization in dispatch without undermining feasibility. Load growth increases total system cost due to higher capital and operating requirements, yet COE changes remain modest, indicating improved utilization of installed assets at higher demand levels. The optimized configuration’s emissions inventory quantifies the residual environmental footprint of the least-cost reliable solution, including 429.2 million kg/yr CO₂, 3.30 million kg/yr SO₂, 0.44 million kg/yr NOₓ, 2.30 million kg/yr CO, 19.7 thousand kg/yr particulate matter, and 122 thousand kg/yr unburned hydrocarbons, reflecting reduced coal combustion through PV displacement during high-resource periods. These findings demonstrate that an optimized PV-coal-grid hybrid can deliver cost-competitive electricity, operational robustness to fuel/resource/demand uncertainty, and measurable multi-pollutant emissions mitigation, offering a realistic transition pathway for coal-reliant systems.